Despite technological advances in public health informatics, the evaluation of infectious disease surveillance systems data remains incomplete. In this study, a thorough evaluation was performed of the West Virginia Electronic Disease Surveillance System (WVEDSS, 2007-2010) and the West Virginia Electronic Disease Surveillance System NationalEDSS -Based System (WVEDSS-NBS; March 2012 - March 2014) for Category II infectious diseases in West Virginia. The purpose was to identify key areas in the surveillance system process from disease diagnosis to disease prevention that need improvement. Grounded in the diffusion of innovation theory, a quasi-experimental, interrupted, time-series design was used to evaluate the 2 data sets. Research questions examined differences in mean reporting time, the 24-hour standard, and comparison of complete fields (DOB, gender etc.) of the data sets using independent samples t tests. The study found (a) that the mean reporting times were shorter for WVEDSS compared to WVEDSS-NBS (p < .05) for all vaccine-preventable infectious diseases (VPID) in Category II except for mumps; (b) that the 24-hour standard was not met for WVEDSS compared to WVEDSS-NBS (p < .05) for all VPID in Category II except for mumps, and (c) that most fields were complete for WVEDSS compared to WVEDSS-NBS (p < .05) for all VPID in Category II except for meningococcal disease. Healthcare professionals in the state can use the results of this research to improve the system attributes of timeliness and completeness. Implications for positive social change included improved access to public health data to better understand health disparities, which, in turn could reduce morbidity and mortality within the population.

Recent advances in information and communication technologies have made the development and operation of complex disease surveillance systems technically feasible, and many systems have been proposed to interpret diverse data sources for health-related signals. Implementing these systems for daily use and efficiently interpreting their output, however, remains a technical challenge. This thesis presents a method for understanding disease surveillance systems structurally, examines four existing systems, and discusses the implications of developing such systems. The discussion is followed by two papers. The first paper describes the design of a national outbreak detection system for daily disease surveillance. It is currently in use at the Swedish Institute for Communicable Disease Control. The source code has been licenced under GNU v3 and is freely available. The second paper discusses methodological issues in computational epidemiology, and presents the lessons learned from a software development project in which a spatially explicit micro-meso-macro model for the entire Swedish population was built based on registry data. / QC 20110520

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